OGER++: Hybrid Multi-type Entity Recognition

Overview

Journal J Cheminform

Publisher Biomed Central

Specialty Chemistry

Date 2019 Jan 23

PMID 30666476

Citations 18

Authors

Lenz Furrer

Anna Jancso

Nicola Colic

Fabio Rinaldi

Affiliations

Soon will be listed here.

Abstract

Background: We present a text-mining tool for recognizing biomedical entities in scientific literature. OGER++ is a hybrid system for named entity recognition and concept recognition (linking), which combines a dictionary-based annotator with a corpus-based disambiguation component. The annotator uses an efficient look-up strategy combined with a normalization method for matching spelling variants. The disambiguation classifier is implemented as a feed-forward neural network which acts as a postfilter to the previous step.

Results: We evaluated the system in terms of processing speed and annotation quality. In the speed benchmarks, the OGER++ web service processes 9.7 abstracts or 0.9 full-text documents per second. On the CRAFT corpus, we achieved 71.4% and 56.7% F1 for named entity recognition and concept recognition, respectively.

Conclusions: Combining knowledge-based and data-driven components allows creating a system with competitive performance in biomedical text mining.

Citing Articles

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Sample Size Considerations for Fine-Tuning Large Language Models for Named Entity Recognition Tasks: Methodological Study.

Majdik Z, Graham S, Edward J, Rodriguez S, Karnes M, Jensen J JMIR AI. 2024; 3:e52095.

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